optim                  package:base                  R Documentation

_G_e_n_e_r_a_l-_p_u_r_p_o_s_e _O_p_t_i_m_i_z_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     General-purpose optimization based on Nelder-Mead, quasi-Newton
     and conjugate-gradient algorithms. It includes an option for
     box-constrained optimization and simulated annealing.

_U_s_a_g_e:

     optim(par, fn, gr = NULL,
           method = c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN"),
           lower = -Inf, upper = Inf,
           control = list(), hessian = FALSE, ...)

_A_r_g_u_m_e_n_t_s:

     par: Initial values for the parameters to be optimized over.

      fn: A function to be minimized (or maximized), with first
          argument the vector of parameters over which minimization is
          to take place. It should return a scalar result.

      gr: A function to return the gradient for the '"BFGS"', '"CG"'
          and '"L-BFGS-B"' methods.  If it is 'NULL', a
          finite-difference approximation will be used.

          For the '"SANN"' method it specifies a function to generate a
          new candidate point.  If it is 'NULL' a default Gaussian
          Markov kernel is used. 

  method: The method to be used. See *Details*.

lower, upper: Bounds on the variables for the '"L-BFGS-B"' method.

 control: A list of control parameters. See *Details*.

 hessian: Logical. Should a numerically differentiated Hessian matrix
          be returned?

     ...: Further arguments to be passed to 'fn' and 'gr'.

_D_e_t_a_i_l_s:

     By default this function performs minimization, but it will
     maximize if 'control$fnscale' is negative.

     The default method is an implementation of that of Nelder and Mead
     (1965), that uses only function values and is robust but
     relatively slow. It will work reasonably well for
     non-differentiable functions.

     Method '"BFGS"' is a quasi-Newton method (also known as a variable
     metric algorithm), specifically that published simultaneously in
     1970 by Broyden, Fletcher, Goldfarb and Shanno. This uses function
     values and gradients to build up a picture of the surface to be
     optimized.

     Method '"CG"' is a conjugate gradients method based on that by
     Fletcher and Reeves (1964) (but with the option of Polak-Ribiere
     or Beale-Sorenson updates).  Conjugate gradient methods will
     generally be more fragile that the BFGS method, but as they do not
     store a matrix they may be successful in much larger optimization
     problems.

     Method '"L-BFGS-B"' is that of Byrd _et. al._ (1994) which allows
     _box constraints_, that is each variable can be given a lower
     and/or upper bound. The initial value must satisfy the
     constraints. This uses a limited-memory modification of the BFGS
     quasi-Newton method. If non-trivial bounds are supplied, this
     method will be selected, with a warning.

     Nocedal and Wright (1999) is a comprehensive reference for the
     previous three methods.

     Method '"SANN"' is by default a variant of simulated annealing
     given in Belisle (1992). Simulated-annealing belongs to the class
     of stochastic global optimization methods. It uses only function
     values but is relatively slow. It will also work for
     non-differentiable functions. This implementation uses the
     Metropolis function for the acceptance probability. By default the
     next candidate point is generated from a Gaussian Markov kernel
     with scale proportional to the actual temperature. If a function
     to generate a new candidate point is given, method '"SANN"' can
     also be used to solve combinatorial optimization problems.
     Temperatures are decreased according to the logarithmic cooling
     schedule as given in Belisle (1992, p. 890). Note that the
     '"SANN"' method depends critically on the settings of the control
     parameters. It is not a general-purpose method but can be  very
     useful in getting to a good value on a very rough surface.

     Function 'fn' can return 'NA' or 'Inf' if the function cannot be
     evaluated at the supplied value, but the initial value must have a
     computable finite value of 'fn'. (Except for method '"L-BFGS-B"'
     where the values should always be finite.)

     'optim' can be used recursively, and for a single parameter as
     well as many.

     The 'control' argument is a list that can supply any of the
     following components:

     '_t_r_a_c_e' Non-negative integer. If positive, tracing information on
          the progress of the optimization is produced. Higher values
          may produce more tracing information: for method '"L-BFGS-B"'
          there are six levels of tracing.  (To understand exactly what
          these do see the source code: higher levels give more
          detail.)

     '_f_n_s_c_a_l_e' An overall scaling to be applied to the value of 'fn'
          and 'gr' during optimization. If negative, turns the problem
          into a maximization problem. Optimization is performed on
          'fn(par)/fnscale'.

     '_p_a_r_s_c_a_l_e' A vector of scaling values for the parameters.
          Optimization is performed on 'par/parscale' and these should
          be comparable in the sense that a unit change in any element
          produces about a unit change in the scaled value.

     '_n_d_e_p_s' A vector of step sizes for the finite-difference
          approximation to the gradient, on 'par/parscale' scale.
          Defaults to '1e-3'.

     '_m_a_x_i_t' The maximum number of iterations. Defaults to '100' for
          the derivative-based methods, and '500' for '"Nelder-Mead"'.
          For '"SANN"' 'maxit' gives the total number of function
          evaluations. There is no other stopping criterion. Defaults
          to '10000'.

     '_a_b_s_t_o_l' The absolute convergence tolerance. Only useful for
          non-negative functions, as a tolerance for reaching zero.

     '_r_e_l_t_o_l' Relative convergence tolerance.  The algorithm stops if
          it is unable to reduce the value by a factor of 'reltol *
          (abs(val) + reltol)' at a step.  Defaults to
          'sqrt(.Machine$double.eps)', typically about '1e-8'.

     '_a_l_p_h_a', '_b_e_t_a', '_g_a_m_m_a' Scaling parameters for the
          '"Nelder-Mead"' method. 'alpha' is the reflection factor
          (default 1.0), 'beta' the contraction factor (0.5) and
          'gamma' the expansion factor (2.0).

     '_R_E_P_O_R_T' The frequency of reports for the '"BFGS"' and
          '"L-BFGS-B"' methods if 'control$trace' is positive. Defaults
          to every 10 iterations.

     '_t_y_p_e' for the conjugate-gradients method. Takes value '1' for the
          Fletcher-Reeves update, '2' for Polak-Ribiere and '3' for
          Beale-Sorenson.

     '_l_m_m' is an integer giving the number of BFGS updates retained in
          the '"L-BFGS-B"' method, It defaults to '5'.

     '_f_a_c_t_r' controls the convergence of the '"L-BFGS-B"' method.
          Convergence occurs when the reduction in the objective is
          within this factor of the machine tolerance. Default is
          '1e7', that is a tolerance of about '1e-8'.

     '_p_g_t_o_l' helps controls the convergence of the '"L-BFGS-B"' method.
          It is a tolerance on the projected gradient in the current
          search direction. This defaults to zero, when the check is
          suppressed.

     '_t_e_m_p' controls the '"SANN"' method. It is the starting
          temperature for the cooling schedule. Defaults to '10'.

     '_t_m_a_x' is the number of function evaluations at each temperature
          for the '"SANN"' method. Defaults to '10'.

_V_a_l_u_e:

     A list with components: 

     par: The best set of parameters found.

   value: The value of 'fn' corresponding to 'par'.

  counts: A two-element integer vector giving the number of calls to
          'fn' and 'gr' respectively. This excludes those calls needed
          to compute the Hessian, if requested, and any calls to 'fn'
          to compute a finite-difference approximation to the gradient.

convergence: An integer code. '0' indicates successful convergence.
          Error codes are

          '_1' indicates that the iteration limit 'maxit' had been
               reached.

          '_1_0' indicates degeneracy of the Nelder-Mead simplex.

          '_5_1' indicates a warning from the '"L-BFGS-B"' method; see
               component 'message' for further details.

          '_5_2' indicates an error from the '"L-BFGS-B"' method; see
               component 'message' for further details.

 message: A character string giving any additional information returned
          by the optimizer, or 'NULL'.

 hessian: Only if argument 'hessian' is true. A symmetric matrix giving
          an estimate of the Hessian at the solution found. Note that
          this is the Hessian of the unconstrained problem even if the
          box constraints are active.

_N_o_t_e:

     'optim' will work with one-dimensional 'par's, but the default
     method does not work well (and will warn).  Use 'optimize'
     instead.

     The code for methods '"Nelder-Mead"', '"BFGS"' and '"CG"' was
     based originally on Pascal code in Nash (1990) that was translated
     by 'p2c' and then hand-optimized.  Dr Nash has agreed that the
     code can be made freely available.

     The code for method '"L-BFGS-B"' is based on Fortran code by Zhu,
     Byrd, Lu-Chen and Nocedal obtained from Netlib (file
     'opt/lbfgs_bcm.shar': another version is in 'toms/778').

     The code for method '"SANN"' was contributed by A. Trapletti.

_R_e_f_e_r_e_n_c_e_s:

     Belisle, C. J. P. (1992) Convergence theorems for a class of
     simulated annealing algorithms on Rd. _J Applied Probability_,
     *29*, 885-895.

     Byrd, R. H., Lu, P., Nocedal, J. and Zhu, C.  (1995) A limited
     memory algorithm for bound constrained optimization. _SIAM J.
     Scientific Computing_, *16*, 1190-1208.

     Fletcher, R. and Reeves, C. M. (1964) Function minimization by
     conjugate gradients. _Computer Journal_ *7*, 148-154.

     Nash, J. C. (1990) _Compact Numerical Methods for Computers.
     Linear Algebra and Function Minimisation._ Adam Hilger.

     Nelder, J. A. and Mead, R. (1965) A simplex algorithm for function
     minimization. _Computer Journal_ *7*, 308-313.

     Nocedal, J. and Wright, S. J. (1999) _Numerical Optimization_.
     Springer.

_S_e_e _A_l_s_o:

     'nlm', 'optimize', 'constrOptim'

_E_x_a_m_p_l_e_s:

     fr <- function(x) {   ## Rosenbrock Banana function
         x1 <- x[1]
         x2 <- x[2]
         100 * (x2 - x1 * x1)^2 + (1 - x1)^2
     }
     grr <- function(x) { ## Gradient of 'fr'
         x1 <- x[1]
         x2 <- x[2]
         c(-400 * x1 * (x2 - x1 * x1) - 2 * (1 - x1),
            200 *      (x2 - x1 * x1))
     }
     optim(c(-1.2,1), fr)
     optim(c(-1.2,1), fr, grr, method = "BFGS")
     optim(c(-1.2,1), fr, NULL, method = "BFGS", hessian = TRUE)
     optim(c(-1.2,1), fr, grr, method = "CG")
     optim(c(-1.2,1), fr, grr, method = "CG", control=list(type=2))
     optim(c(-1.2,1), fr, grr, method = "L-BFGS-B")

     flb <- function(x)
         { p <- length(x); sum(c(1, rep(4, p-1)) * (x - c(1, x[-p])^2)^2) }
     ## 25-dimensional box constrained
     optim(rep(3, 25), flb, NULL, "L-BFGS-B",
           lower=rep(2, 25), upper=rep(4, 25)) # par[24] is *not* at boundary

     ## "wild" function , global minimum at about -15.81515
     fw <- function (x)
         10*sin(0.3*x)*sin(1.3*x^2) + 0.00001*x^4 + 0.2*x+80
     plot(fw, -50, 50, n=1000, main = "optim() minimising 'wild function'")

     res <- optim(50, fw, method="SANN",
                  control=list(maxit=20000, temp=20, parscale=20))
     res
     ## Now improve locally
     (r2 <- optim(res$par, fw, method="BFGS"))
     points(r2$par, r2$val, pch = 8, col = "red", cex = 2)

     ## Combinatorial optimization: Traveling salesman problem
     library(mva) # normally loaded
     library(ts)  # for embed, normally loaded

     data(eurodist)
     eurodistmat <- as.matrix(eurodist)

     distance <- function(sq) {  # Target function
         sq2 <- embed(sq, 2)
         return(sum(eurodistmat[cbind(sq2[,2],sq2[,1])]))
     }

     genseq <- function(sq) {  # Generate new candidate sequence 
         idx <- seq(2, NROW(eurodistmat)-1, by=1)
         changepoints <- sample(idx, size=2, replace=FALSE)
         tmp <- sq[changepoints[1]]
         sq[changepoints[1]] <- sq[changepoints[2]]
         sq[changepoints[2]] <- tmp
         return(sq)
     }

     sq <- c(1,2:NROW(eurodistmat),1)  # Initial sequence
     distance(sq)

     set.seed(2222) # chosen to get a good soln quickly
     res <- optim(sq, distance, genseq, method="SANN",
                  control = list(maxit=6000, temp=2000, trace=TRUE))
     res  # Near optimum distance around 12842

     loc <- cmdscale(eurodist)
     rx <- range(x <- loc[,1])
     ry <- range(y <- -loc[,2])
     tspinit <- loc[sq,]
     tspres <- loc[res$par,]
     s <- seq(NROW(tspres)-1)

     plot(x, y, type="n", asp=1, xlab="", ylab="",
          main="initial solution of traveling salesman problem")
     arrows(tspinit[s,1], -tspinit[s,2], tspinit[s+1,1], -tspinit[s+1,2],
            angle=10, col="green") 
     text(x, y, names(eurodist), cex=0.8)

     plot(x, y, type="n", asp=1, xlab="", ylab="",
          main="optim() 'solving' traveling salesman problem") 
     arrows(tspres[s,1], -tspres[s,2], tspres[s+1,1], -tspres[s+1,2],
            angle=10, col="red")
     text(x, y, names(eurodist), cex=0.8)

